The task of relation extraction is to find relation tuples from free text. [1]}, [2]}, and [3]} proposed the idea of distant supervision to automatically obtain a large amount of training data for this task. The idea is to map the relation tuples in existing knowledge bases (KBs) to text corpora such as Wikipedia or news articles. The assumption is that if the subject and object entities of a relation tuple appear in a sentence, then this sentence can be considered as providing evidence of the relation between the entities. Most relation extraction work focuses on such distantly supervised sentence-level extraction. This method can give a significantly large amount of training data to build supervised relation extraction models. But the assumption of distant supervision that the two entities of a tuple must appear in the same sentence is overly strict. We may not find an adequate number of evidence sentences for many relations if the two entities in a relation do not appear in the same sentence. The relation extraction models built on such data can find relations only for a small number of relations and most relations of the KBs will be out of the reach of such models.
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